the interactive map on FEMA’s website
We downloaded FEMA data from this link: https://www.fema.gov/openfema-data-page/public-assistance-funded-projects-details-v1
This .csv file include detailed public assistance funded projects information. We selected the the Sever Storm(s), Hurricane and Coastal Storm incident-types in 2009-2018 for this analysis.
This dataset is prepared for county-level plot.
| DisasterNum | Year | Incident | State | County | ProjectAmount | FederalShareObligated | ToatlObligated |
|---|---|---|---|---|---|---|---|
| 1871 | 2010 | Severe Storm(s) | North Carolina | Madison | 5356.79 | 4017.59 | 4017.59 |
| 1871 | 2010 | Severe Storm(s) | North Carolina | Madison | 5163.41 | 3872.56 | 3872.56 |
| 1871 | 2010 | Severe Storm(s) | North Carolina | Madison | 13811.83 | 10358.87 | 10358.87 |
| 1871 | 2010 | Severe Storm(s) | North Carolina | Madison | 3765.00 | 2823.75 | 2823.75 |
| 1871 | 2010 | Severe Storm(s) | North Carolina | Madison | 12318.38 | 9238.79 | 9238.79 |
| 1871 | 2010 | Severe Storm(s) | North Carolina | Madison | 7488.04 | 5616.03 | 5616.03 |
Here are some explanation from FEMA website:
projectAmount(“Project Amount”): The estimated total cost of the Public Assistance grant project in dollars, without administrative costs. This amount is based on the damage survey.
federalShareObligated(“Federal Share Obligated”): The Public Assistance grant funding available to the grantee (State) in dollars, for sub-grantee’s approved Project Worksheets.
totalObligated(“Total Obligated”): The federal share of the Public Assistance grant eligible project amount in dollars, plus grantee (State) and sub-grantee (applicant) administrative costs. The federal share is typically 75% of the total cost of the project.
In this plot, we take all years(2009-2018) data, calculated the summary of obligated amount of each county.
In order to give an obvious plot of each county, we delete some outlier points.
# outliers
outliers <- hrccJ %>% filter(TotalAmount %in% outValue)
hrccJ <- hrccJ %>% filter(!(TotalAmount %in% outValue))
kable(head(outliers),"html",align = "c")| GEO_ID | state | county | TotalAmount |
|---|---|---|---|
| 01003 | Alabama | Baldwin | 13845.087 |
| 01015 | Alabama | Calhoun | 7403.059 |
| 01039 | Alabama | Covington | 5297.541 |
| 01043 | Alabama | Cullman | 13460.415 |
| 01049 | Alabama | DeKalb | 6700.742 |
| 01059 | Alabama | Franklin | 19455.656 |
Also add some popups to show the State, County and Total amount(in thousands).
Plot the leaflet.
Leaflet css file: Per Liedman
https://gist.github.com/perliedman/9d42f6d1d6583e882df7ae9e95252c0d
Spatial data is everywhere. When we want to compare the subjects from different areas, creating a map is much more intuitive than just looking at the bland data set. And that’s why interactive mapping tools like leaflet is powerful. In the projects we done, we mainly focused on plotting polygons and paths. In leaflet’s official website, we found that there are many others functions that can be done by leaflet. How could we draw the map that best illustrates our data? How could we prevent our map from being too fancy and uninformative?